BUSseq
Bioc currentBatch Effect Correction with Unknow Subtypes for scRNA-seq data
Release Lineage
Entered 3.14 · Oct 27, 2021
Current · Requires R 4.6
Description
BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
88 15 exported
Complexity
4.5 avg / 15 max
Call network
88 nodes / 55 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
6,795
Files
34
Compiled share
64.3%
Has compiled src
Yes
Language breakdown
API
Exported functions
15
Internal functions
0
Testing & CI
Has tests
Yes
Test-to-code ratio
0.12
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
3.6
System requirements
–
C++ standard
C++11
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
10
First release
2021-10-26
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Examples that run
- 82%
- Documented parameters
- 99%
- Return-value docs
- 100%
- References docs
- 100%
Topics
People
- Fangda Song author maintainer
- Ga Ming Chan author
- Yingying Wei author
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("BUSseq")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.